[1]} proposed a hierarchical reinforcement learning-based (RL) deep neural model for joint entity and relation extraction. A high-level RL is used to identify the relation based on the relation-specific tokens in the sentences. After a relation is identified, a low-level RL is used to extract the two entities associated with the relation using a sequence labeling approach. This process is repeated multiple times to extract all the relation tuples present in the sentences. A special None relation is used to identify no relation situation in the sentences. Entities extracted associated with the None relations are ignored. [2]} used a graph convolutional network (GCN) where they treated each token in a sentence as a node in a graph and edges were considered as relations. [3]} used an N-gram attention mechanism with an encoder-decoder model for the completion of knowledge bases using distantly supervised data. [4]} used the encoder-decoder framework for this task where they used a CNN-based multi-label classifier to find all the relations first, then used multi-head attention [5]} to extract the entities corresponding to each relation.
